PNNL

Abstract

The prevailing approach in the use of sensor arrays for identification is that the array must be trained to recognize the vapor or vapors of interest. In this essentially empirical approach, compounds not in the training set cannot be identified. The sensor array, however, is collecting multivariate chemical information about the sample. In principle, information should be extractable from the array response to gain knowledge of the chemical properties of the sample. Polymer-coated acoustic wave sensors represent a sensor technology that is particularly well characterized in terms of the sensors' transduction mechanisms and the interactions of analyte species with the polymeric sensing layers. Examples of acoustic wave devices used in chemical sensing applications include the thickness shear mode (TSM), surface acoustic wave (SAW), and the flexural plate wave (FPW) device. Acoustic wave vapor sensors respond to any vapor that is sorbed at the sensing surface with a response that is proportional to the amount of vapor sorbed. In this paper we will demonstrate how SIMCA, inverse least squares (ILS), and classical least squares (CLS) appoaches can be applied to data from a well-understood polymer-coated acoustic wave vapors, whether that vapor was in the training set or not. We will also present preliminary studies demonstrating quantification of vapors that were not in the training set.